1. Field of the Invention
The invention herein relates generally to radar systems and methods including Frequency Modulated Continuous Wave (FMCW) radar techniques and Angle of Arrival interferometry processing techniques for use in three dimensional imaging radar systems in general, and more specifically those used in vehicles as sensors for machine environmental awareness.
2. Description of the Prior Art
A RADAR (RAdio Detection And Ranging) system senses its environment by transmitting electromagnetic energy, and receives the subsequent reflected energy from objects in its local environment. The radar then processes this information into usable data to be delivered to a system controller or operator for use. Radar transmitters can apply many different frequencies and modulation techniques, and various types of usable data can be created, typically including range, azimuth, elevation and relative radial motion, depending on the desired end use of the system.
In recent years, radar use in automobiles has been increasing significantly for improved automation and safety. The automotive industry is investing heavily in improving sensing technology performance as situational awareness for onboard computer systems is considered insufficient. While radar, Lidar, cameras and ultrasonic sensors all have their own strengths, radar is considered an essential sensing technology due to its ability to measure distances and relative speed directly and can see through dust, fog, snow and rain at long distances,
Ideally, an automotive radar will provide 360 degree three-dimensional awareness with fine detail around the vehicle. However this type of imaging radar is, at best, extremely expensive to generate the detailed information required quickly to be useful. To date commercial radar systems have a limited field of view and generally are not able to resolve target detail or specific location within the radar beam. In particular, measuring the height of a target is very difficult without using sophisticated imaging techniques.
The most common type of automotive radar used today is a relatively short range fixed millimeter-wave Frequency Modulated Continuous Wave (FMCW) radar which provides accurate range and relative target size within a predefined transmit beam and within a relatively short radius, but does not generally determine location within the beam or height of targets. To compensate for this limited resolution, automakers often use many radars of varying beam widths, effective range and pointing directions to improve environmental awareness around the vehicle, Yet these remain as relatively coarse measurements that often do not include precise location or elevation information. This lack of resolution is driving the automotive industry to develop radar systems with improved performance,
The Problem
Presently, the most common area of research for automotive radar system development is applying phased array and synthetic aperture radar imaging techniques to fixed multi-element arrays positioned at multiple locations around the vehicle. These designs are based on imaging arrays previously developed by governmental defense and aeronautic organizations to image target areas from remote platforms. These systems do image the target environment in great detail and would be a superb solution for sensing the immediate environment. However, even these advanced radar systems, some with multi-million dollar budgets, record the data for post-processing which is unacceptable for automotive use. Phased array antenna systems steer a beam electrically without having to move the antenna elements. These arrays can scan in both azimuth and elevation simultaneously at the speed of the electrical switching circuits. Additionally, complex information received from an array can be processed using aperture synthesis based algorithms to form a Real Aperture Radar that can image targets in great detail. In both cases, to increase angular resolution of such an array, the radar designer will increase the number of elements in both dimensions, but this will be at the expense of an exponential increase in the computational resources required and/or additional time for processing, and its associated increase in cost. To estimate the number of elements the designer must choose the angular resolution and wavelength of the system.
Hardware Complexity Estimation
By using a form of the Rayleigh Criterion, an estimation of array size is given by:
                              N          B                =                                            N              E              2                        -                          N              E                                2                                    Equation        ⁢                                  ⁢        2            Where:D=Width of the array (m)λ=Wavelength (m)θ=Desired resolution in radians
As an example, an automotive radar designer specifies an angular resolution of 1.0 degree (0.0175 radians) in both azimuth and elevation with a wavelength of 4 mm, the minimum aperture size D will be 1.22 (4 mm/0.0175)=279 mm. Typically an array will have elements spaced no more than every 0.5 λ so the number of elements in a single dimension will be (279 mm/4 mm)/0.5=140. For a rectangular array with an azimuth and elevation resolution of 1.0×1.0 degrees the number of elements required is therefore 140×140 or 19,600.
It should be noted that in order for the array to function properly, each array element must have its own unique receiver circuitry to down-convert the RF and record the data. In the above example, if all the components associated with each element were to cost $1, the array would cost nearly $20,000. It quickly becomes apparent how the cost of such an array is prohibitive for use in commercial automobiles.
Computation Load Estimation
Real aperture imaging requires processing complex data from baselines formed by element pairs. If a single calculation process is defined as the amount of processing required for a single baseline, or element pair, then the total number of such processes can be estimated by calculating the number of baselines in any given array. The number of unique baselines in any given array is:
                    D        +                  1.22          ⁢                      λ            θ                                              Equation        ⁢                                  ⁢        1            Where:NB=Number of baselines in the arrayNE=Number of elements in the array.
Using equation 2, the number of processes can be calculated for a 19,600 element array which will require over 192 million unique processes. Since each sensor will likely create a dataset of 2048 unique 16 bit numbers, or more, to be used for each complex baseline process, one can easily see that a Real Aperture computational load is presently too large to process onboard a consumer vehicle. Additionally, an automotive radar needs rapid updates which may be 5 or more complete environmental updates per second with a processing delay of no more than 0.1 second or less to ensure the data is meaningful when used. Further, if radar coverage of more than +/−60 degrees is required, additional radar arrays will need to be added with their associated costs.
Thus, there are two primary limitations for high resolution automotive radar imaging: (i) insufficient onboard computational capacity and/or the high price of high speed computational hardware; and (ii) The high cost of advanced phased array elements required to image broad areas of the immediate environment with sufficient detail.
These challenges present the need for low cost alternative automotive radar architectures that can provide high resolution range, azimuth, elevation and signal strength data immediately surrounding the vehicle, and which can process and update this data fast enough for immediate use.